Workload Characteristics Based Performance Optimization for Edge Intelligence

Edge intelligence refers to a form of service that uses artificial intelligence algorithms to provide data analysis capabilities for network edge devices.However, the edge computing environment is more complex and changeable than cloud computing.There are many problems in the process of building edge intelligence, such as the lack of quantitative evaluation standards, heterogeneous computing platforms, complex network topologies, and changing user needs.Among them, the more prominent is the contradiction between the high resource demand of the algorithm model and the low resource reserve of edge devices.Machine lear-ning is the main workload of edge intelligence.It requires a lot of computing resources.However, the computing resources of edge devices are limited, and the supply and demand between the two do not match.The deployment and optimization of edge intelligent load has become a problem.Therefore, in response to the problem of edge intelligent load performance optimization, this paper proposes cloud-ed